{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=10, micro=14, releaselevel='final', serial=0)\n",
      "matplotlib 3.10.0\n",
      "numpy 1.26.4\n",
      "pandas 2.2.3\n",
      "sklearn 1.6.0\n",
      "torch 2.5.1+cu124\n",
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "\n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)\n",
    "\n",
    "seed = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c29f1d6fe09a9955",
   "metadata": {},
   "source": [
    "# 数据加载\n",
    "\n",
    "- 采用WMT16的德语和英语平行语料库，数据集主页：[WMT16](https://www.statmt.org/wmt16/multimodal-task.html#task1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d047d1eed0336fbe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:46.447355Z",
     "start_time": "2025-02-07T11:31:46.443797Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:41:27.849004Z",
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     "shell.execute_reply": "2025-02-07T16:41:27.850859Z",
     "shell.execute_reply.started": "2025-02-07T16:41:27.848982Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # 调用脚本文件\n",
    "# !pip install sacremoses\n",
    "# !sh data_multi30k.sh wmt16 wmt16_cut de en"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a930b5668e422750",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c0f0f4781f5e042f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:46.530455Z",
     "start_time": "2025-02-07T11:31:46.522333Z"
    },
    "execution": {
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     "iopub.status.busy": "2025-02-07T16:41:27.851915Z",
     "iopub.status.idle": "2025-02-07T16:41:27.860008Z",
     "shell.execute_reply": "2025-02-07T16:41:27.859545Z",
     "shell.execute_reply.started": "2025-02-07T16:41:27.852061Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "\n",
    "class LangPairDataset(Dataset):\n",
    "    def __init__(self, mode=\"train\", max_length=128,\n",
    "                 overwrite_cache=False, data_dir=\"wmt16\"):\n",
    "        self.data_dir = Path(data_dir)\n",
    "        cache_path = self.data_dir / \".cache\" / f\"de2en_{mode}_{max_length}.npy\"\n",
    "\n",
    "        if overwrite_cache or not cache_path.exists():\n",
    "            # parents=True：如果父目录不存在，会自动创建所有必要的父目录。\n",
    "            # exist_ok=True：如果目录已经存在，不会抛出错误。\n",
    "            cache_path.parent.mkdir(parents=True, exist_ok=True)\n",
    "            # 读取源语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_src.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.src = f.readlines()\n",
    "            # 读取目标语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_trg.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.trg = f.readlines()\n",
    "\n",
    "            filtered_src = []\n",
    "            filtered_trg = []\n",
    "            # max length filter,超出最大长度的句子舍弃\n",
    "            for src, trg in zip(self.src, self.trg):\n",
    "                # 过滤长度超过最大长度的句子\n",
    "                if len(src) <= max_length and len(trg) <= max_length:\n",
    "                    filtered_src.append(src.strip())  # 去掉句子前后的空格\n",
    "                    filtered_trg.append(trg.strip())\n",
    "            filtered_src = np.array(filtered_src)\n",
    "            filtered_trg = np.array(filtered_trg)\n",
    "            #allow_pickle=True允许保存对象数组，将过滤后的数据保存为 NumPy 数组，存储在缓存文件中\n",
    "            np.save(\n",
    "                cache_path,\n",
    "                {\"src\": filtered_src, \"trg\": filtered_trg},\n",
    "                allow_pickle=True\n",
    "            )\n",
    "            print(f\"save cache to {cache_path}\")\n",
    "\n",
    "        else:\n",
    "            # allow_pickle=True允许保存对象数组\n",
    "            cache_dict = np.load(cache_path, allow_pickle=True).item()\n",
    "            print(f\"load {mode} dataset from {cache_path}\")\n",
    "            filtered_src = cache_dict[\"src\"]\n",
    "            filtered_trg = cache_dict[\"trg\"]\n",
    "\n",
    "        self.src = filtered_src\n",
    "        self.trg = filtered_trg\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return self.src[index], self.trg[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.src)\n",
    "\n",
    "# train_ds = LangPairDataset(\"train\")\n",
    "# val_ds = LangPairDataset(\"val\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2a0f23be5871b84",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:46.613997Z",
     "start_time": "2025-02-07T11:31:46.610152Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:41:27.860887Z",
     "iopub.status.busy": "2025-02-07T16:41:27.860544Z",
     "iopub.status.idle": "2025-02-07T16:41:27.862988Z",
     "shell.execute_reply": "2025-02-07T16:41:27.862511Z",
     "shell.execute_reply.started": "2025-02-07T16:41:27.860858Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(len(train_ds))\n",
    "# print(len(val_ds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eaf27846b953da4",
   "metadata": {},
   "source": [
    "## Tokenizer\n",
    "\n",
    "这里有两种处理方式，分别对应着 encoder 和 decoder 的 word embedding 是否共享，这里实现共享的方案"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "df6173e2f201c301",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:46.688652Z",
     "start_time": "2025-02-07T11:31:46.657013Z"
    },
    "execution": {
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     "shell.execute_reply": "2025-02-07T16:41:27.890435Z",
     "shell.execute_reply.started": "2025-02-07T16:41:27.863839Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 18107/18107 [00:00<00:00, 941934.10it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vocab_size: 18111\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "word2idx = {\n",
    "    \"[PAD]\": 0,  # 填充 token\n",
    "    \"[BOS]\": 1,  # begin of sentence\n",
    "    \"[UNK]\": 2,  # 未知 token\n",
    "    \"[EOS]\": 3,  # end of sentence\n",
    "}\n",
    "\n",
    "idx2word = {value: key for key, value in word2idx.items()}\n",
    "index = len(idx2word)\n",
    "threshold = 1  # 出现次数低于此的token舍弃\n",
    "\n",
    "with open(\"wmt16/vocab\", \"r\", encoding=\"utf8\") as fd:\n",
    "    for line in tqdm(fd.readlines()):\n",
    "        token, counts = line.strip().split()\n",
    "        if int(counts) >= threshold:\n",
    "            word2idx[token] = index\n",
    "            idx2word[index] = token\n",
    "            index += 1\n",
    "\n",
    "vocab_size = len(word2idx)\n",
    "print(\"vocab_size: {}\".format(vocab_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "461056213fba3ed4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:46.755648Z",
     "start_time": "2025-02-07T11:31:46.745669Z"
    },
    "execution": {
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     "shell.execute_reply": "2025-02-07T16:41:27.899725Z",
     "shell.execute_reply.started": "2025-02-07T16:41:27.891560Z"
    }
   },
   "outputs": [],
   "source": [
    "class Tokenizer:\n",
    "    def __init__(self, word2idx, idx2word, max_length=128,\n",
    "                 pad_idx=0, bos_idx=1, eos_idx=3, unk_idx=2):\n",
    "        self.word2idx = word2idx\n",
    "        self.idx2word = idx2word\n",
    "        self.max_length = max_length\n",
    "        self.pad_idx = pad_idx\n",
    "        self.bos_idx = bos_idx\n",
    "        self.eos_idx = eos_idx\n",
    "        self.unk_idx = unk_idx\n",
    "\n",
    "    def encode(self, text_list, padding_first=False, add_bos=True, add_eos=True,\n",
    "               return_mask=False):\n",
    "        # 如果padding_first == True，则padding加载前面，否则加载后面\n",
    "        # return_mask: 是否返回mask(掩码），mask用于指示哪些是padding的，哪些是真实的token \n",
    "        # 若启动bos_eos，则在句首和句尾分别添加bos和eos，则 add_bos=True, add_eos=True即都为1\n",
    "        max_length = min(self.max_length, add_bos + add_eos + max([len(text) for text in text_list]))\n",
    "        indices_list = []\n",
    "        for text in text_list:\n",
    "            # 如果词表中没有这个词，就用unk_idx代替，indices是一个list,里面是每个词的index,也就是一个样本的index\n",
    "            indices = [self.word2idx.get(word, self.unk_idx) for word in text[:max_length - add_eos - add_bos]]\n",
    "            if add_bos:\n",
    "                indices = [self.bos_idx] + indices\n",
    "            if add_eos:\n",
    "                indices = indices + [self.eos_idx]\n",
    "            if padding_first:  # padding加载前面，超参可以调\n",
    "                indices = [self.pad_idx] * (max_length - len(indices)) + indices\n",
    "            else:  # padding加载后面\n",
    "                indices = indices + [self.pad_idx] * (max_length - len(indices))\n",
    "            indices_list.append(indices)\n",
    "        input_ids = torch.tensor(indices_list)  # 转换为tensor\n",
    "        # mask是一个和input_ids一样大小的tensor，0代表token，1代表padding，mask用于去除padding的影响\n",
    "        masks = (input_ids == self.pad_idx).to(dtype=torch.float64)\n",
    "        return input_ids if not return_mask else (input_ids, masks)\n",
    "\n",
    "    def decode(self, indices_list, remove_bos=True, remove_eos=True, remove_pad=True, split=False):\n",
    "        text_list = []\n",
    "        for indices in indices_list:\n",
    "            text = []\n",
    "            for index in indices:\n",
    "                # 如果词表中没有这个词，就用unk_idx代替\n",
    "                word = self.idx2word.get(index, \"[UNK]\")\n",
    "                if remove_bos and word == \"[BOS]\":\n",
    "                    continue\n",
    "                if remove_eos and word == \"[EOS]\":  # 如果到达eos，就结束\n",
    "                    break\n",
    "                if remove_pad and word == \"[PAD]\":  # 如果到达pad，就结束\n",
    "                    break\n",
    "                text.append(word)  # 单词添加到列表中\n",
    "            # 把列表中的单词拼接，变为一个句子\n",
    "            text_list.append(\" \".join(text) if not split else text)\n",
    "        return text_list\n",
    "\n",
    "\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word)\n",
    "\n",
    "# tokenizer.encode([[\"hello\"], [\"hello\", \"world\"]], add_bos=True, add_eos=False)\n",
    "# raw_text = [\"hello world\".split(), \"tokenize text datas with batch\".split(), \"this is a test\".split()]\n",
    "# indices = tokenizer.encode(raw_text, padding_first=False, add_bos=True, add_eos=True)\n",
    "# decode_text = tokenizer.decode(indices.tolist(), remove_bos=False, remove_eos=False, remove_pad=False)\n",
    "# print(\"raw text\")\n",
    "# for raw in raw_text:\n",
    "#     print(raw)\n",
    "# print(\"indices\")\n",
    "# for index in indices:\n",
    "#     print(index)\n",
    "# print(\"decode text\")\n",
    "# for decode in decode_text:\n",
    "#     print(decode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "628dded826e86608",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:46.794562Z",
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     "iopub.status.busy": "2025-02-07T16:41:27.902002Z",
     "iopub.status.idle": "2025-02-07T16:41:27.904416Z",
     "shell.execute_reply": "2025-02-07T16:41:27.903902Z",
     "shell.execute_reply.started": "2025-02-07T16:41:27.902299Z"
    }
   },
   "outputs": [],
   "source": [
    "# for i,j in train_ds:\n",
    "#     print(len(i))\n",
    "#     print(len(j))\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d43df9ce1d4860bb",
   "metadata": {},
   "source": [
    "## Transformer Batch Sampler\n",
    "\n",
    "> Sentence pairs were batched together by approximate sequence length. Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens\n",
    "句子按照序列长度差不多的分到一个批次。 每个训练批次包含一组句子对，其中包含大约 25000 个源标记和 25000 个目标标记"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7c072c0e714bd8ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:46.886273Z",
     "start_time": "2025-02-07T11:31:46.879588Z"
    },
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     "iopub.status.busy": "2025-02-07T16:41:27.904929Z",
     "iopub.status.idle": "2025-02-07T16:41:27.911563Z",
     "shell.execute_reply": "2025-02-07T16:41:27.911059Z",
     "shell.execute_reply.started": "2025-02-07T16:41:27.905079Z"
    }
   },
   "outputs": [],
   "source": [
    "# 下面的info对象\n",
    "class SampleInfo:\n",
    "    def __init__(self, i, lens):\n",
    "        \"\"\"\n",
    "        记录文本对的序号和长度信息\n",
    "        输入：\n",
    "            - i (int): 文本对的序号。\n",
    "            - lens (list): 文本对源语言和目标语言的长度\n",
    "        \"\"\"\n",
    "        self.i = i\n",
    "        # 加一是考虑填补在文本前后的特殊词元，lens[0]和lens[1]分别表示源语言和目标语言的长度\n",
    "        self.max_len = max(lens[0], lens[1]) + 1\n",
    "        self.src_len = lens[0] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "        self.trg_len = lens[1] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "\n",
    "\n",
    "# 一个批量生成器，根据词元数目的限制来控制批量的大小。\n",
    "# 它会根据传入的样本信息，在不超过设定大小的情况下，逐步构建批量。\n",
    "class TokenBatchCreator:\n",
    "    def __init__(self, batch_size):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        batch_size (int): 用于限制批量的大小。\n",
    "        功能:\n",
    "        初始化了一个空的批量列表 _batch。\n",
    "        设定了初始的最大长度为 -1。\n",
    "        存储了传入的 batch_size。\n",
    "        \"\"\"\n",
    "        self._batch = []  # 这个就是之前的batch_size，就是一个batch内有多少个样本\n",
    "        self.max_len = -1\n",
    "        self.batch_size = batch_size  # 限制批量的大小,假设是4096\n",
    "\n",
    "    def append(self, info: SampleInfo):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        info (SampleInfo): 文本对的信息。\n",
    "        功能:\n",
    "        接收一个 SampleInfo 对象，并根据其最大长度信息更新当前批量的最大长度。\n",
    "        如果将新的样本加入批量后超过了批量大小限制，它会返回已有的批量并将新的样本加入新的批量。\n",
    "        否则，它会更新最大长度并将样本添加到当前批量中。\n",
    "        \"\"\"\n",
    "        # 更新当前批量的最大长度\n",
    "        cur_len = info.max_len  # 当前样本的长度\n",
    "        max_len = max(self.max_len, cur_len)  # 每来一个样本，更新当前批次的最大长度\n",
    "\n",
    "        # 如果新的样本加入批量后超过大小限制，则将已有的批量返回，新的样本加入新的批量\n",
    "        # 同一批样本计算时，短的样本向最长的样本对齐，所以用max_len来计算\n",
    "        if max_len * (len(self._batch) + 1) > self.batch_size:\n",
    "            # result_batch保存原有的_batch，_batch清空\n",
    "            self._batch, result_batch = [], self._batch\n",
    "            self._batch.append(info)  #箱子里的第一条样本，放入\n",
    "            # 因为是当前batch的第一个样本，所以它的长度就是当前长度\n",
    "            self.max_len = cur_len\n",
    "            return result_batch\n",
    "        else:\n",
    "            self.max_len = max_len\n",
    "            self._batch.append(info)\n",
    "            return None\n",
    "\n",
    "    # 将类的方法转换为属性，从而实现对类属性的访问、修改和删除的控制\n",
    "    @property\n",
    "    def batch(self):\n",
    "        return self._batch"
   ]
  },
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   "id": "c1dd026c49a90114",
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   "source": [
    "from torch.utils.data import BatchSampler\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "class TransformerBatchSampler(BatchSampler):\n",
    "    def __init__(self, dataset, batch_size, shuffle_batch=False,\n",
    "                 clip_last_batch=False, seed=0):\n",
    "        \"\"\"\n",
    "        批量采样器\n",
    "        输入:\n",
    "            - dataset: 数据集\n",
    "            - batch_size: 批量大小\n",
    "            - shuffle_batch: 是否对生成的批量进行洗牌\n",
    "            - clip_last_batch: 是否裁剪最后剩下的数据\n",
    "            - seed: 随机数种子\n",
    "        \"\"\"\n",
    "        self._dataset = dataset\n",
    "        self._batch_size = batch_size\n",
    "        self._shuffle_batch = shuffle_batch\n",
    "        self._clip_last_batch = clip_last_batch\n",
    "        self._seed = seed\n",
    "        self._random = np.random\n",
    "        self._random.seed(seed)\n",
    "\n",
    "        self._sample_infos = []\n",
    "        # 根据数据集中的每个样本，创建了对应的 SampleInfo 对象，包含了样本的索引和长度信息\n",
    "        for i, data in enumerate(self._dataset):\n",
    "            lens = [len(data[0]), len(data[1])]  #输入和输出的长度计算放到lens中\n",
    "            self._sample_infos.append(SampleInfo(i, lens))\n",
    "\n",
    "    def __iter__(self):\n",
    "        \"\"\"\n",
    "        对数据集中的样本进行排序，排序规则是先按源语言长度排序，如果相同则按目标语言长度排序。\n",
    "        使用 TokenBatchCreator 逐步组装批量数据，当满足批量大小时返回一个批量的样本信息。\n",
    "        如果不裁剪最后一个批次的数据且存在剩余样本，则将这些样本组成最后一个批次。\n",
    "        如果需要对批量进行洗牌，则对批次进行洗牌操作。\n",
    "        通过迭代器，抛出每个批量的样本在数据集中的索引。\n",
    "        \"\"\"\n",
    "        # 排序，如果源语言长度相同则按照目标语言的长度排列\n",
    "        infos = sorted(self._sample_infos,\n",
    "                       key=lambda x: (x.src_len, x.trg_len))\n",
    "        # 组装批量，所有的batch都放入batch_infos\n",
    "        batch_infos = []\n",
    "        # 批量生成器\n",
    "        batch_creator = TokenBatchCreator(self._batch_size)\n",
    "        for info in infos:\n",
    "            batch = batch_creator.append(info)\n",
    "            # 存够一个batch的样本信息后，会把这个batch返回，否则返回为None\n",
    "            if batch is not None:\n",
    "                batch_infos.append(batch)\n",
    "\n",
    "        # 是否抛弃最后批量的文本对\n",
    "        # 如果最后一个batch的样本数目不足一个batch，则将其剩余的样本作为最后一个batch\n",
    "        if not self._clip_last_batch and len(batch_creator.batch) != 0:\n",
    "            batch_infos.append(batch_creator.batch)  # 最后一个batch\n",
    "\n",
    "        # 打乱batch\n",
    "        # 这里的shuffle是对batch_infos进行shuffle，而不是对batch_infos中的样本进行shuffle\n",
    "        if self._shuffle_batch:\n",
    "            self._random.shuffle(batch_infos)\n",
    "\n",
    "        self.batch_number = len(batch_infos)\n",
    "\n",
    "        # 抛出一个批量的文本对在数据集中的序号\n",
    "        for batch in batch_infos:\n",
    "            # info.i 是文本对的索引\n",
    "            batch_indices = [info.i for info in batch]\n",
    "            yield batch_indices\n",
    "\n",
    "    def __len__(self):\n",
    "        \"\"\"\n",
    "        返回批量的数量\n",
    "        \"\"\"\n",
    "        if hasattr(self, \"batch_number\"):\n",
    "            return self.batch_number\n",
    "        # 计算批量的数量,没有用到下面的情况，不用看\n",
    "        batch_number = (len(self._dataset) +\n",
    "                        self._batch_size) // self._batch_size\n",
    "        return batch_number\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 10,
   "id": "71ae51fb9433db98",
   "metadata": {
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   "outputs": [],
   "source": [
    "# sampler = TransformerBatchSampler(train_ds, batch_size=4096, shuffle_batch=True)\n",
    "# \n",
    "# #为什么这里每个批量的样本对数目不一样呢？长度*batch_number>4096的时候，就会返回上一个batch，然后新的样本加入新的batch,具体要看TokenBatchCreator的40行\n",
    "# for idx, batch in enumerate(sampler):\n",
    "#     print(\"第{}批量的数据中含有文本对是：{}，数量为：{}\".format(idx, batch, len(batch)))\n",
    "#     if idx >= 3:\n",
    "#         break\n",
    "# \n",
    "# print(\"-\"*50)\n",
    "# print(len(sampler))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98b1add788035b2a",
   "metadata": {},
   "source": [
    "## DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "24e99727ef49605",
   "metadata": {
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   "source": [
    "def collate_fn(batch, tokenizer):\n",
    "    # 取batch内第0列进行分词，赋给src_words\n",
    "    src_words = [pair[0].split() for pair in batch]\n",
    "    # 取batch内第1列进行分词，赋给trg_words\n",
    "    trg_words = [pair[1].split() for pair in batch]\n",
    "\n",
    "    # paddings 在前在后影响不大，但在后好理解，所以padding_first=False\n",
    "    # [BOS] src [EOS] [PAD] \n",
    "    encoder_inputs, encoder_inputs_mask = tokenizer.encode(\n",
    "        src_words, padding_first=False, add_bos=True, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    # 目标语言 [BOS] trg [PAD]\n",
    "    decoder_inputs = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=True, add_eos=False, return_mask=False\n",
    "    )\n",
    "\n",
    "    # 用于后续计算loss\n",
    "    # trg [EOS] [PAD]\n",
    "    decoder_labels, decoder_labels_mask = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=False, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    return {\n",
    "        \"encoder_inputs\": encoder_inputs.to(device),\n",
    "        \"encoder_inputs_mask\": encoder_inputs_mask.to(device),\n",
    "        \"decoder_inputs\": decoder_inputs.to(device),\n",
    "        \"decoder_labels\": decoder_labels.to(device),\n",
    "        \"decoder_labels_mask\": decoder_labels_mask.to(device),\n",
    "    }  # 当返回的数据较多时，用dict返回比较合理\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f53f13a987ea9201",
   "metadata": {
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   "source": [
    "from functools import partial  # 固定collate_fct的tokenizer参数\n",
    "\n",
    "# # 可以调大batch_size,来看最终的bleu，如果GPU内存不够，可以减小batch_size\n",
    "# sampler = TransformerBatchSampler(train_ds, batch_size=256, shuffle_batch=True)\n",
    "# \n",
    "# # batch_sampler 是一个自定义的批次采样器，用于控制如何从数据集中采样批次。与 batch_size 和 shuffle 不同，batch_sampler 可以更灵活地定义批次的组织方式（如按长度分组）。\n",
    "# # partial(collate_fn, tokenizer=tokenizer) 使用 functools.partial 将 tokenizer 参数固定到 collate_fn 中，生成一个新的函数。\n",
    "# sample_dl = DataLoader(train_ds, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "# \n",
    "# for batch in sample_dl:  #外层是拿每个batch\n",
    "#     for key, value in batch.items():  #内层是拿每个batch里面是一个字典\n",
    "#         print(key)\n",
    "#         print(\"-\" * 50)\n",
    "#         print(value)\n",
    "#         print(\"-\" * 50)\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa4932b257d82059",
   "metadata": {},
   "source": [
    "# 定义模型\n",
    "\n",
    "- Transformer模型由Embedding、Transformer-Block组成\n",
    "- Embedding包括：\n",
    "    - WordEmbedding\n",
    "    - PositionEmbedding\n",
    "- Transformer-Block包括：\n",
    "    - Self-Attention\n",
    "    - Cross-Attention\n",
    "    - MLP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d2988138abac64d",
   "metadata": {},
   "source": [
    "## Embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "847a70137968848a",
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    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class TransformerEmbedding(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.hidden_size = config[\"d_model\"]  # 词向量维度\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "\n",
    "        # layers,设置padding_idx可以让pad的词向量全为0\n",
    "        self.word_embedding = nn.Embedding(\n",
    "            self.vocab_size, self.hidden_size, padding_idx=self.pad_idx\n",
    "        )\n",
    "        self.position_embedding = nn.Embedding(\n",
    "            self.max_length, self.hidden_size,\n",
    "            # 位置编码，权重通过get_positional_encoding函数计算得到\n",
    "            _weight=self.get_position_encoding(self.max_length, self.hidden_size)\n",
    "        )\n",
    "\n",
    "        self.position_embedding.weight.requires_grad_(False)  # 不更新位置编码的权重\n",
    "        self.dropout = nn.Dropout(dropout_rate)  # 随机失活层\n",
    "\n",
    "    def get_word_embedding_weight(self):\n",
    "        return self.word_embedding.weight\n",
    "\n",
    "    # 计算位置信息\n",
    "    # 用于定义类方法（class method）。\n",
    "    # 类方法是绑定到类而不是实例的方法，可以通过类本身或类的实例调用。\n",
    "    @classmethod\n",
    "    def get_position_encoding(self, max_length, hidden_size):\n",
    "        # max_length是最大长度，hidden_size是embedding维度相等\n",
    "        pe = torch.zeros(max_length, hidden_size)  # 初始化位置编码\n",
    "        # .unsqueeze(1) 是将这个一维张量转换为二维张量，\n",
    "        # 即将其形状从 (max_length,) 变为 (max_length, 1)。\n",
    "        # 这个操作在张量的维度上增加了一个维度，使其从一维变为二维，第二维的大小为 1。\n",
    "        position = torch.arange(0, max_length).unsqueeze(1)  # 位置信息,从0到max_length-1\n",
    "        # 计算位置编码的权重,为了性能考量（是数学上的对数函数分解），这里用了log函数\n",
    "        # torch.arange(0, hidden_size, 2) 在 PyTorch 中用于创建一个一维张量，包含从 0 开始到（但不包括）hidden_size 的数值，步长为 2\n",
    "        div_term = torch.exp(\n",
    "            torch.arange(0, hidden_size, 2) *\n",
    "            -(torch.log(torch.Tensor([10000.0])) / hidden_size)\n",
    "        )\n",
    "        pe[:, 0::2] = torch.sin(position * div_term)  # 偶数位置\n",
    "        pe[:, 1::2] = torch.cos(position * div_term)  # 奇数位置\n",
    "        return pe\n",
    "\n",
    "    def forward(self, input_ids):\n",
    "        # input_ids: [batch_size,seq_len]\n",
    "        seq_len = input_ids.shape[1]\n",
    "        assert (\n",
    "                seq_len <= self.max_length), f\"input sequence length should no more than {self.max_length} but got {seq_len}\"\n",
    "\n",
    "        position_ids = torch.arange(seq_len, dtype=torch.long, device=input_ids.device)  # 位置信息\n",
    "        # unsqueeze(0): 在第一维添加一个维度，使张量的形状从 [seq_len] 变为 [1, seq_len]\n",
    "        # expand_as(input_ids): 扩展张量的形状，使其与 input_ids 张量的形状相同\n",
    "        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "\n",
    "        # print(input_ids.shape) #为了调试\n",
    "        # print(position_ids.shape) #为了调试\n",
    "        # print(position_ids) #为了调试\n",
    "\n",
    "        # embedding层\n",
    "        word_embeds = self.word_embedding(input_ids)  # 词嵌入\n",
    "        # word_embeds: [batch_size,seq_len,hidden_size]\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "        pos_embeds = self.position_embedding(position_ids)  # 位置嵌入 # ？\n",
    "        # pos_embeds: [batch_size,seq_len,hidden_size]   \n",
    "        embeds = word_embeds + pos_embeds  # 相加得到嵌入向量\n",
    "        # embeds: [batch_size,seq_len,hidden_size]\n",
    "        embeds = self.dropout(embeds)  # 随机失活\n",
    "        return embeds\n",
    "\n",
    "\n",
    "# #随机input，调用TransformerEmbedding\n",
    "# config={\n",
    "#     \"vocab_size\": 100,\n",
    "#     \"d_model\": 128,\n",
    "#     \"pad_idx\": 0,\n",
    "#     \"max_length\": 64,\n",
    "#     \"dropout\": 0.1,\n",
    "# }\n",
    "# input_ids = torch.randint(0, 100, (2, 50))\n",
    "# embeds = TransformerEmbedding(config)(input_ids)\n",
    "# embeds.shape\n",
    "\n",
    "# 绘制位置编码\n",
    "def plot_position_embedding(position_embedding):\n",
    "    plt.pcolormesh(position_embedding)  # 绘制位置编码矩阵\n",
    "    plt.xlabel('Depth')\n",
    "    plt.ylabel('Position')\n",
    "    plt.colorbar()  # 颜色条，-1到1的颜色范围\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "position_embedding = TransformerEmbedding.get_position_encoding(64, 128)\n",
    "plot_position_embedding(position_embedding)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39630e37b910bd7f",
   "metadata": {},
   "source": [
    "## Transformer Block"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72e7edad4645f763",
   "metadata": {},
   "source": [
    "### scaled-dot-product-attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7bd08ede5d628f45",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:47.273361Z",
     "start_time": "2025-02-07T11:31:47.262137Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:41:28.087316Z",
     "iopub.status.busy": "2025-02-07T16:41:28.087019Z",
     "iopub.status.idle": "2025-02-07T16:41:28.173899Z",
     "shell.execute_reply": "2025-02-07T16:41:28.173314Z",
     "shell.execute_reply.started": "2025-02-07T16:41:28.087284Z"
    }
   },
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "from typing import Optional, Tuple\n",
    "\n",
    "Tensor = torch.Tensor\n",
    "\n",
    "\n",
    "# 修饰器 @dataclass 会自动生成 __init__、__repr__ 和 __eq__ 等方法，减少了样板代码的编写\n",
    "@dataclass\n",
    "class AttentionOutput:\n",
    "    hidden_states: Tensor\n",
    "    attn_scores: Tensor\n",
    "\n",
    "\n",
    "class MultiHeadAttention(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        assert (\n",
    "                self.hidden_size % self.num_heads == 0\n",
    "        ), \"Hidden size must be divisible by num_heads but got {} and {}\".format(\n",
    "            self.hidden_size, self.num_heads)\n",
    "        self.head_dim = self.hidden_size*2 // self.num_heads  # 每个头的维度\n",
    "\n",
    "        # layers\n",
    "        # 第二个self.hidden_size可以*系数\n",
    "        self.Wq = nn.Linear(self.hidden_size, self.hidden_size*2, bias=False)\n",
    "        self.Wk = nn.Linear(self.hidden_size, self.hidden_size*2, bias=False)\n",
    "        self.Wv = nn.Linear(self.hidden_size, self.hidden_size*2, bias=False)\n",
    "        self.Wo = nn.Linear(self.hidden_size*2, self.hidden_size, bias=False)  # 输出层\n",
    "\n",
    "    # -> Tensor 是一种类型注解，表示函数的返回值类型是 Tensor\n",
    "    def _split_heads(self, x: Tensor) -> Tensor:\n",
    "        # 若hidden_size是512\n",
    "        bs, seq_len, _ = x.shape  # [batch_size,seq_len,hidden_size]\n",
    "        # 先将x变形为[batch_size,seq_len,num_heads,head_dim]\n",
    "        x = x.view(bs, seq_len, self.num_heads, self.head_dim)\n",
    "        # num_heads是8，head_dim是64\n",
    "        return x.permute(0, 2, 1, 3)\n",
    "        # 变换维度，[batch_size, num_heads, seq_len, head_dim]\n",
    "\n",
    "    def _merge_heads(self, x: Tensor) -> Tensor:\n",
    "        # 将多头注意力的输出合并为一个张量\n",
    "        bs, _, seq_len, _ = x.shape  # [batch_size, num_heads, seq_len, head_dim]\n",
    "        # 变换维度，变为[batch_size, seq_len, hidden_size]\n",
    "        return x.permute(0, 2, 1, 3).reshape(bs, seq_len, self.hidden_size*2)\n",
    "\n",
    "    def forward(self, querys, keys, values, attn_mask=None) -> AttentionOutput:\n",
    "        # split heads\n",
    "        # [batch_size, seq_len,hidden_dim]->[batch_size, num_heads, seq_len, head_dim*2]\n",
    "        querys = self._split_heads(self.Wq(querys))\n",
    "        keys = self._split_heads(self.Wk(keys))\n",
    "        values = self._split_heads(self.Wv(values))\n",
    "\n",
    "        # calculate attention scores\n",
    "        # 计算注意力分数，matmul是矩阵乘法(在后两个维度上进行)，mT是矩阵转置,\n",
    "        # qk_logits是[batch_size, num_heads, seq_len, seq_len]\n",
    "        qk_logits = torch.matmul(querys, keys.mT)\n",
    "        if attn_mask is not None:\n",
    "            # seq_len*seq_len的部分是有效的\n",
    "            attn_mask = attn_mask[:, :, :querys.shape[-2], :keys.shape[-2]]  #切片\n",
    "            qk_logits += attn_mask * -1e9  # 给需要mask的地方设置一个负无穷\n",
    "        # 计算注意力数,softmax是在seq_len维度上进行的\n",
    "        attn_scores = F.softmax(qk_logits / (self.head_dim ** 0.5), dim=-1)\n",
    "        # attn_scores: [batch_size, num_heads, seq_len, seq_len]\n",
    "\n",
    "        # softmax后的结果与value相乘，得到新的表示\n",
    "        embeds = torch.matmul(attn_scores, values)\n",
    "        # embeds的尺寸是[batch_size, num_heads, seq_len, head_dim*2]\n",
    "        # _merge_heads(embeds)的输出是[batch_size, seq_len, hidden_size*2]\n",
    "        embeds = self.Wo(self._merge_heads(embeds))  # 输出层\n",
    "        # embeds: [batch_size, seq_len, hidden_size]\n",
    "\n",
    "        return AttentionOutput(hidden_states=embeds, attn_scores=attn_scores)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a9eb177f33d1050d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:47.282189Z",
     "start_time": "2025-02-07T11:31:47.275368Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:41:28.174741Z",
     "iopub.status.busy": "2025-02-07T16:41:28.174509Z",
     "iopub.status.idle": "2025-02-07T16:41:28.180926Z",
     "shell.execute_reply": "2025-02-07T16:41:28.180310Z",
     "shell.execute_reply.started": "2025-02-07T16:41:28.174721Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key_value.shape torch.Size([2, 4, 2])\n",
      "torch.Size([2, 3, 2])\n",
      "torch.Size([2, 2, 3, 4])\n"
     ]
    }
   ],
   "source": [
    "mha = MultiHeadAttention({\"num_heads\": 2, \"d_model\": 2})\n",
    "query = torch.randn(2, 3, 2)  # [batch_size, seq_len, hidden_size]\n",
    "query /= query.norm(dim=-1, keepdim=True)  # 归一化\n",
    "key_value = torch.randn(2, 4, 2)\n",
    "print(f'key_value.shape {key_value.shape}')\n",
    "outputs = mha(query, key_value, key_value)  #最终输出shape和query的shape一样\n",
    "print(outputs.hidden_states.shape)\n",
    "print(outputs.attn_scores.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e37f29d98cf51bac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:47.310225Z",
     "start_time": "2025-02-07T11:31:47.303201Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:41:28.181794Z",
     "iopub.status.busy": "2025-02-07T16:41:28.181595Z",
     "iopub.status.idle": "2025-02-07T16:41:28.187390Z",
     "shell.execute_reply": "2025-02-07T16:41:28.186839Z",
     "shell.execute_reply.started": "2025-02-07T16:41:28.181769Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.1597, 0.1062, 0.7341],\n",
      "        [0.1253, 0.3475, 0.5272]])\n"
     ]
    }
   ],
   "source": [
    "#随机一个2阶张量，在-1维度计算softmax\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = torch.randn(2, 3)\n",
    "x_softmax = F.softmax(x, dim=-1)\n",
    "print(x_softmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b451e28eb10aaf26",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:47.593085Z",
     "start_time": "2025-02-07T11:31:47.340239Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:41:28.188441Z",
     "iopub.status.busy": "2025-02-07T16:41:28.188030Z",
     "iopub.status.idle": "2025-02-07T16:41:28.422731Z",
     "shell.execute_reply": "2025-02-07T16:41:28.422210Z",
     "shell.execute_reply.started": "2025-02-07T16:41:28.188420Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.subplots() 用于创建子图网格，其维度基于 outputs.attn_scores.shape[:2]。子图的行数和列数似乎由 outputs.attn_scores 的前两个维度确定。\n",
    "fig, axis = plt.subplots(*outputs.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs.attn_scores.shape[1]):\n",
    "        # axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())：此行使用 Matplotlib 的 matshow 绘制每个 i 和 j 的注意力分数热图。detach().numpy() 将 PyTorch 张量转换为 NumPy 数组以进行可视化。\n",
    "        axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs.attn_scores.shape[2]):\n",
    "            for y in range(outputs.attn_scores.shape[3]):\n",
    "                # axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")：此代码在热图上叠加文本，显示 (x, y) 位置处的注意力分数。格式化部分 f\"{outputs.attn_scores[i, j, x, y]:.2f}\" 确保以两位小数显示注意力分数。文本以白色居中显示在 (y, x) 坐标处。\n",
    "                axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")\n",
    "fig.suptitle(\"multi head attention without mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "42dc1608bbcd426c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:47.842819Z",
     "start_time": "2025-02-07T11:31:47.594089Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:41:28.423540Z",
     "iopub.status.busy": "2025-02-07T16:41:28.423344Z",
     "iopub.status.idle": "2025-02-07T16:41:28.658311Z",
     "shell.execute_reply": "2025-02-07T16:41:28.657618Z",
     "shell.execute_reply.started": "2025-02-07T16:41:28.423520Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mask\n",
    "mask = torch.Tensor([[0, 0, 1, 1], [0, 0, 0, 1], [0, 0, 0, 0]]).reshape(1, 1, 3, 4)  #手工构造mask\n",
    "outputs_masked = mha(query, key_value, key_value, mask)\n",
    "\n",
    "fig, axis = plt.subplots(*outputs_masked.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs_masked.attn_scores.shape[1]):\n",
    "        axis[i, j].matshow(outputs_masked.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs_masked.attn_scores.shape[2]):\n",
    "            for y in range(outputs_masked.attn_scores.shape[3]):\n",
    "                axis[i, j].text(y, x, f\"{outputs_masked.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\",\n",
    "                                color=\"w\")\n",
    "fig.suptitle(\"multi head attention with mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd2b7a6a479f877c",
   "metadata": {},
   "source": [
    "### Transformer-Block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2247e4861550df31",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:47.852677Z",
     "start_time": "2025-02-07T11:31:47.843835Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:41:28.659433Z",
     "iopub.status.busy": "2025-02-07T16:41:28.659094Z",
     "iopub.status.idle": "2025-02-07T16:41:28.669248Z",
     "shell.execute_reply": "2025-02-07T16:41:28.668708Z",
     "shell.execute_reply.started": "2025-02-07T16:41:28.659409Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerBlockOutput:\n",
    "    # 用于存储某个块产生的隐藏状态。\n",
    "    hidden_states: Tensor\n",
    "    # 包含了自注意力机制（self-attention）所计算得到的注意力分数\n",
    "    self_attn_scores: Tensor\n",
    "    # 是一个可选字段，存储了交叉注意力（cross-attention）计算得到的注意力分数。\n",
    "    # 这里的 Optional 表示这个字段可以是 Tensor 类型，也可以是 None。\n",
    "    cross_attn_scores: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerBlock(nn.Module):\n",
    "    def __init__(self, config, add_cross_attention=False):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        dropout_rate = config[\"dropout\"]  # 随机失活率\n",
    "        ffn_dim = config[\"dim_feedforward\"]  # 前馈网络的维度\n",
    "        eps = config[\"layer_norm_eps\"]  # 层归一化的epsilon值\n",
    "\n",
    "        # self-attention\n",
    "        self.self_attn = MultiHeadAttention(config)  # 多头注意力\n",
    "        self.self_ln = nn.LayerNorm(self.hidden_size, eps=eps)  # 层归一化(层标准化)\n",
    "        self.self_dropout = nn.Dropout(dropout_rate)  # 随机失活\n",
    "\n",
    "        # cross-attention，交叉注意力，decoder中使用,因此额外做一个判断\n",
    "        if add_cross_attention:\n",
    "            self.cross_attn = MultiHeadAttention(config)\n",
    "            self.cross_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "            self.cross_dropout = nn.Dropout(dropout_rate)\n",
    "        else:\n",
    "            self.cross_attn = None\n",
    "\n",
    "        # FFN,前馈神经网络\n",
    "        self.ffn = nn.Sequential(\n",
    "            nn.Linear(self.hidden_size, ffn_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(ffn_dim, self.hidden_size),\n",
    "        )\n",
    "        self.ffn_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "        self.ffn_dropout = nn.Dropout(dropout_rate)\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            hidden_states,\n",
    "            attn_mask=None,\n",
    "            encoder_outputs=None,\n",
    "            cross_attn_mask=None,\n",
    "    ):\n",
    "        # self-attention,自注意力\n",
    "        self_attn_outputs = self.self_attn(\n",
    "            hidden_states, hidden_states, hidden_states, attn_mask\n",
    "        )  # self_attn_outputs 是 AttentionOutput 类型\n",
    "        self_embeds = self.self_ln(\n",
    "            hidden_states + self.self_dropout(self_attn_outputs.hidden_states)\n",
    "        )  #多头注意力进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "\n",
    "        # cross-attention，交叉注意力\n",
    "        if self.cross_attn is not None:\n",
    "            assert encoder_outputs is not None, \"encoder_outputs should not be None\"\n",
    "            cross_attn_outputs = self.cross_attn(\n",
    "                self_embeds, encoder_outputs, encoder_outputs, cross_attn_mask\n",
    "            )  # query是self_embeds，key和value都是encoder_outputs\n",
    "            cross_embeds = self.cross_ln(\n",
    "                self_embeds + self.cross_dropout(cross_attn_outputs.hidden_states)\n",
    "            )  # 交叉注意力进行dropout，然后和self_embeds进行残差连接，然后进行层归一化\n",
    "\n",
    "        # FFN\n",
    "        # 如果有交叉注意力，则使用交叉注意力的输出作为FFN的输入；否则，使用self_embeds作为FFN的输入\n",
    "        embeds = cross_embeds if self.cross_attn is not None else self_embeds\n",
    "        # embeds:[batch_size, seq_len, hidden_size]\n",
    "        ffn_output = self.ffn(embeds)  # 前馈神经网络\n",
    "        # ffn_output:[batch_size, seq_len, hidden_size]\n",
    "        # 前馈神经网络进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "        embeds = self.ffn_ln(embeds + self.ffn_dropout(ffn_output))\n",
    "\n",
    "        return TransformerBlockOutput(\n",
    "            hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_outputs.attn_scores,\n",
    "            cross_attn_scores=cross_attn_outputs.attn_scores if self.cross_attn is not None else None,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19be62a364cb5cc2",
   "metadata": {},
   "source": [
    "## Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4f0e09edb3e7bbfe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:47.861662Z",
     "start_time": "2025-02-07T11:31:47.854683Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:41:28.670102Z",
     "iopub.status.busy": "2025-02-07T16:41:28.669834Z",
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     "shell.execute_reply": "2025-02-07T16:41:28.675283Z",
     "shell.execute_reply.started": "2025-02-07T16:41:28.670081Z"
    }
   },
   "outputs": [],
   "source": [
    "from typing import List\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class TransformerEncoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerEncoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_encoder_layers\"]\n",
    "\n",
    "        # layers,仅仅是一个模块的列表，它本身没有定义前向传递（forward pass）过程。你需要在 forward 方法中明确地定义如何使用这些模块。\n",
    "        self.layers = nn.ModuleList(\n",
    "            [TransformerBlock(config) for _ in range(self.num_layers)]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self, encoder_inputs_embeds, attn_mask=None\n",
    "    ) -> TransformerEncoderOutput:\n",
    "        # 存储每个层的注意力分数\n",
    "        attn_scores = []\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = encoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config)\n",
    "            block_outputs = layer(embeds, attn_mask=attn_mask)\n",
    "            # 在每个层的输出中，提取了隐藏状态 block_outputs.hidden_states，\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            # 并将对应的注意力分数 block_outputs.self_attn_scores 添加到列表 attn_scores 中。\n",
    "            attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的注意力分数,用于画图\n",
    "\n",
    "        return TransformerEncoderOutput(\n",
    "            last_hidden_states=embeds, attn_scores=attn_scores\n",
    "        )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a142b31a53d27997",
   "metadata": {},
   "source": [
    "## Decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c0e7f64561cbd5f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:47.868778Z",
     "start_time": "2025-02-07T11:31:47.862665Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:41:28.676771Z",
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     "shell.execute_reply": "2025-02-07T16:41:28.682276Z",
     "shell.execute_reply.started": "2025-02-07T16:41:28.676750Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerDecoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    self_attn_scores: List[Tensor]\n",
    "    cross_attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerDecoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_decoder_layers\"]\n",
    "\n",
    "        # layers\n",
    "        self.layers = nn.ModuleList(\n",
    "            [\n",
    "                TransformerBlock(config, add_cross_attention=True)\n",
    "                for _ in range(self.num_layers)\n",
    "            ]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            decoder_inputs_embeds,\n",
    "            encoder_outputs,\n",
    "            attn_mask=None,\n",
    "            cross_attn_mask=None,\n",
    "    ) -> TransformerDecoderOutput:\n",
    "        self_attn_scores = []  # 存储每个层的自注意力分数\n",
    "        cross_attn_scores = []  # 存储每个层的交叉注意力分数\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = decoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config, add_cross_attention=True)\n",
    "            # 为什么交叉注意力的mask要传入cross_attn_mask而不是attn_mask？\n",
    "            # 因为计算MutilHeadAttention的时候,keys和values都是encoder_outputs，query是decoder的输出，因此需要传入cross_attn_mask。\n",
    "            block_outputs = layer(\n",
    "                embeds,\n",
    "                attn_mask=attn_mask,  # 自注意力的mask\n",
    "                encoder_outputs=encoder_outputs,\n",
    "                cross_attn_mask=cross_attn_mask,  # 交叉注意力的mask\n",
    "            )\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            self_attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的自注意力分数\n",
    "            cross_attn_scores.append(block_outputs.cross_attn_scores)  # 存储每个层的交叉注意力分数\n",
    "\n",
    "        return TransformerDecoderOutput(\n",
    "            last_hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_scores,\n",
    "            cross_attn_scores=cross_attn_scores,\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c345d9c0e326a13",
   "metadata": {},
   "source": [
    "#### mask\n",
    "\n",
    "- mask实际上大类上只有两种\n",
    "    1. `padding_mask`：mask掉`pad_idx`，不计算损失\n",
    "    2. `attention_mask`：mask掉`pad_idx`，不计算注意力分数\n",
    "- Decoder的`attention_mask`和Encoder有一定的区别：\n",
    "    - Encoder可以同时看见序列所有信息，故只mask掉`pad_idx`\n",
    "    - Decoder只能看到在自身之前的序列的信息，故要额外mask掉自身之后的序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9baa438dbfbcdd8a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:47.875250Z",
     "start_time": "2025-02-07T11:31:47.869783Z"
    },
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     "shell.execute_reply": "2025-02-07T16:41:28.690056Z",
     "shell.execute_reply.started": "2025-02-07T16:41:28.686099Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[False, False, False, False, False],\n",
      "        [ True, False, False, False, False],\n",
      "        [ True,  True, False, False, False],\n",
      "        [ True,  True,  True, False, False],\n",
      "        [ True,  True,  True,  True, False]])\n",
      "--------------------------------------------------\n",
      "tensor([[False,  True,  True,  True,  True],\n",
      "        [False, False,  True,  True,  True],\n",
      "        [False, False, False,  True,  True],\n",
      "        [False, False, False, False,  True],\n",
      "        [False, False, False, False, False]])\n"
     ]
    }
   ],
   "source": [
    "print((torch.triu(torch.ones(5, 5)) == 0))\n",
    "print(\"-\" * 50)\n",
    "# 符合Decoder的attention_mask形式\n",
    "print((torch.triu(torch.ones(5, 5)) == 0).transpose(-1, -2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "52343e686b4eed69",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:48.043509Z",
     "start_time": "2025-02-07T11:31:47.876264Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:41:28.691687Z",
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     "shell.execute_reply": "2025-02-07T16:41:28.839342Z",
     "shell.execute_reply.started": "2025-02-07T16:41:28.691658Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def generate_square_subsequent_mask(sz: int) -> Tensor:\n",
    "    # The masked positions are filled with True.\n",
    "    # Unmasked positions are filled with False.\n",
    "    # torch.ones(sz, sz): 创建一个全为 1 的 sz × sz 的矩阵。\n",
    "    # torch.triu(...): 使用 triu 函数取得矩阵的上三角部分，将主对角线以下部分置零。\n",
    "    mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "    return mask\n",
    "\n",
    "\n",
    "#画出一个16×16的矩阵热力图，黄色部分为True，是掩码\n",
    "plt.matshow(generate_square_subsequent_mask(16))\n",
    "plt.colorbar()\n",
    "plt.xlabel(\"keys\")\n",
    "plt.ylabel(\"querys\")\n",
    "plt.title(\"1 means mask while 0 means unmask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2548d49c0f7a8fd5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:48.562667Z",
     "start_time": "2025-02-07T11:31:48.044511Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:41:28.840963Z",
     "iopub.status.busy": "2025-02-07T16:41:28.840755Z",
     "iopub.status.idle": "2025-02-07T16:41:29.335171Z",
     "shell.execute_reply": "2025-02-07T16:41:29.334524Z",
     "shell.execute_reply.started": "2025-02-07T16:41:28.840943Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['[BOS]', '[UNK]', 'quick', 'brown', '[UNK]', 'jumps', 'over', 'the', '[UNK]', 'dog', '.', '[EOS]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "['[BOS]', '[UNK]', 'does', 'the', '[UNK]', 'say', '?', '[EOS]', '[PAD]', '[PAD]', '[PAD]', '[PAD]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#通过下面代码查看mask的效果\n",
    "inputs_words = [\"The quick brown fox jumps over the lazy dog .\", \"What does the fox say ?\"]\n",
    "\n",
    "inputs_ids, inputs_mask = tokenizer.encode([w.split() for w in inputs_words], return_mask=True)\n",
    "for i in range(len(inputs_ids)):\n",
    "    decode_text = \\\n",
    "        tokenizer.decode(inputs_ids[i:i + 1].tolist(), remove_bos=False, remove_eos=False, remove_pad=False,\n",
    "                         split=True)[0]\n",
    "    print(decode_text)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    print(inputs_mask[i].reshape(1, -1))\n",
    "    print(\"-\" * 50)\n",
    "    # reshape(1, -1):将 input_mask[i] 的形状调整为 (1, sequence_length)\n",
    "    # repeat_interleave 在第 0 维（dim=0）上重复 sequence_length 次，生成形状为 (sequence_length, sequence_length) 的掩码。\n",
    "    self_attn_mask = inputs_mask[i].reshape(1, -1).repeat_interleave(inputs_ids.shape[-1], dim=0)\n",
    "    print(self_attn_mask)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    look_ahead_mask = generate_square_subsequent_mask(inputs_ids.shape[-1])\n",
    "\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "    axs[0].matshow(self_attn_mask)\n",
    "    axs[0].set_title(\"self_attn_mask\")\n",
    "    axs[0].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_ylabel(\"querys\")\n",
    "    axs[0].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_xlabel(\"keys\")\n",
    "    axs[1].matshow(look_ahead_mask)\n",
    "    axs[1].set_title(\"look_ahead_mask\")\n",
    "    axs[1].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_ylabel(\"querys\")\n",
    "    axs[1].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_xlabel(\"keys\")\n",
    "    plt.show()\n",
    "    print('-' * 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14c5b12ee51329e8",
   "metadata": {},
   "source": [
    "## Transformer Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "89a32ab3ca75bf0c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:48.580468Z",
     "start_time": "2025-02-07T11:31:48.564673Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:41:29.336555Z",
     "iopub.status.busy": "2025-02-07T16:41:29.336258Z",
     "iopub.status.idle": "2025-02-07T16:41:29.356389Z",
     "shell.execute_reply": "2025-02-07T16:41:29.355547Z",
     "shell.execute_reply.started": "2025-02-07T16:41:29.336525Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerOutput:\n",
    "    logits: Tensor\n",
    "    encoder_last_hidden_states: Tensor\n",
    "    encoder_attn_scores: List[Tensor]  #画图\n",
    "    decoder_last_hidden_states: Tensor\n",
    "    decoder_self_attn_scores: List[Tensor]  #画图\n",
    "    decoder_cross_attn_scores: List[Tensor]  #画图\n",
    "    preds: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerModel(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]\n",
    "        self.num_encoder_layers = config[\"num_encoder_layers\"]\n",
    "        self.num_decoder_layers = config[\"num_decoder_layers\"]\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        self.bos_idx = config[\"bos_idx\"]\n",
    "        self.eos_idx = config[\"eos_idx\"]\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "        # 是否共享词嵌入，\n",
    "        # 共享后，decoder的词嵌入层和encoder的词嵌入层相同，节省内存\n",
    "        self.share = config[\"share_embedding\"]\n",
    "\n",
    "        # layers\n",
    "        self.src_embedding = TransformerEmbedding(config)  # 输入的嵌入层\n",
    "        # 如果共享词嵌入，则使用src_embedding作为trg_embedding\n",
    "        if self.share:\n",
    "            # 源和目标的嵌入层相同，共享参数，节省内存\n",
    "            self.trg_embedding = self.src_embedding\n",
    "            self.linear = lambda x: torch.matmul(\n",
    "                # x是该层的输入，[batch_size, seq_len, hidden_size]\n",
    "                # self.trg_embedding.get_word_embedding_weight().T: [hidden_size,vocab_size]\n",
    "                x, self.trg_embedding.get_word_embedding_weight().T\n",
    "            )  # 输出层，共享参数，直接拿原有embedding矩阵的转置，节省内存\n",
    "        else:\n",
    "            self.trg_embedding = TransformerEmbedding(config)  # decoder模块的嵌入层\n",
    "            self.linear = nn.Linear(self.hidden_size, self.vocab_size)  # 输出层\n",
    "\n",
    "        self.encoder = TransformerEncoder(config)\n",
    "        self.decoder = TransformerDecoder(config)\n",
    "\n",
    "        self._init_weights()  # init weights\n",
    "\n",
    "    def _init_weights(self):\n",
    "        # self.parameters(): 这个方法返回模型中的所有可学习参数（权重和偏置）。\n",
    "        for p in self.parameters():\n",
    "            if p.dim() > 1:  # 如果参数是二维或更高维（通常是权重矩阵），则执行初始化。\n",
    "                nn.init.xavier_uniform_(p)  # 使用 xavier 均匀分布来初始化权重\n",
    "\n",
    "    def generate_square_subsequent_mask(self, sz: int) -> Tensor:\n",
    "        # 为了生成斜三角的mask\n",
    "        mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "        return mask\n",
    "\n",
    "    # 用于训练,在TransformerBlock中的每一层内是并行的，层间是串行的\n",
    "    def forward(\n",
    "            self, encoder_inputs, decoder_inputs, encoder_inputs_mask=None\n",
    "    ) -> TransformerOutput:\n",
    "        # encoder_inputs: [batch_size, src_len]\n",
    "        # decoder_inputs: [batch_size, trg_len]\n",
    "        # encoder_inputs_mask: [batch_size, src_len]\n",
    "        if encoder_inputs_mask is None:\n",
    "            # 返回一个布尔张量，形状与 encoder_inputs 相同。\n",
    "            # 该布尔张量的每个位置会根据 encoder_inputs 中的值是否等于 self.pad_idx 来设置：\n",
    "            # 如果相等，值为 True（表示该位置是填充位置）。\n",
    "            # 如果不相等，值为 False（表示该位置是有效输入） \n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        # unsqueeze(1): 这个操作在张量的第 1 个维度上增加一个新的维度\n",
    "        #    得到 (batch_size, 1, src_len)。\n",
    "        # unsqueeze(2): 这个操作在张量的第 2 个维度上再次增加一个新的维度。\n",
    "        #    得到 (batch_size, 1, 1, src_len)。\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(decoder_inputs.shape[-1])  # [trg_len, trg_len]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(decoder_inputs.device)\n",
    "        )  # [1, 1, trg_len, trg_len] 用于decoder的自注意力\n",
    "\n",
    "        # 增加decoder_inputs_mask和look_ahead_mask进行组合\n",
    "        decoder_inputs_mask = decoder_inputs.eq(self.pad_idx)\n",
    "        # [batch_size, trg_len]，和上面encoder_inputs_mask一致\n",
    "        decoder_inputs_mask = decoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, trg_len]\n",
    "        decoder_inputs_mask = decoder_inputs_mask + look_ahead_mask\n",
    "        # [batch_size, 1, 1, trg_len]与[1, 1, trg_len, trg_len]相加，得到decoder的自注意力mask\n",
    "        # decoder_inputs_mask : [batch_size, 1, trg_len,trg_len]\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        encoder_outputs = self.encoder(\n",
    "            encoder_inputs_embeds, attn_mask=encoder_inputs_mask\n",
    "        )  # encoder_inputs_mask用于encoder的自注意力,广播去做计算 \n",
    "\n",
    "        # decoding\n",
    "        # decoder_inputs_embeds是TransformerEmbedding(config)\n",
    "        decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "        # decoder是TransformerDecoder(config)\n",
    "        decoder_outputs = self.decoder(\n",
    "            decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "            encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "            attn_mask=decoder_inputs_mask,  # 用于decoder的自注意力,广播去做计算\n",
    "            cross_attn_mask=encoder_inputs_mask,  # 用于decoder的交叉注意力,广播去做计算\n",
    "        )\n",
    "        # decoder_outputs.last_hidden_states: [batch_size, trg_len, hidden_size]\n",
    "        logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "        # [batch_size, trg_len, vocab_size]\n",
    "\n",
    "        return TransformerOutput(\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n",
    "\n",
    "    @torch.no_grad()  # 禁用梯度计算\n",
    "    def infer(self, encoder_inputs, encoder_inputs_mask=None) -> TransformerOutput:\n",
    "        # 应对多个样本同时进行推理\n",
    "        if encoder_inputs_mask is None:\n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(self.max_length)\n",
    "        # [max_length, max_length]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(encoder_inputs.device)\n",
    "        )  # [1, 1, max_length, max_length] 用于decoder的自注意力\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        # 因为只支持单样本预测，没有paddings，所以不需要mask\n",
    "        encoder_outputs = self.encoder(encoder_inputs_embeds)\n",
    "        # encoder_outputs.last_hidden_states: [batch_size, src_len, hidden_size]\n",
    "\n",
    "        # decoding,多样本推理\n",
    "        # encoder_inputs.shape[0]: 获取编码器输入的批量大小（batch size），即样本的数量。\n",
    "        # .reshape(-1, 1): 这个操作将张量的形状调整为 (batch_size, 1)，\n",
    "        decoder_inputs = torch.Tensor([self.bos_idx] * encoder_inputs.shape[0]).reshape(-1, 1).long().to(\n",
    "            device=encoder_inputs.device)\n",
    "        # 推理是串行的，每次只推理一个token，所以需要初始化decoder_inputs为bos_idx\n",
    "        for cur_len in tqdm(range(1, self.max_length + 1)):\n",
    "            decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "            decoder_outputs = self.decoder(\n",
    "                decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "                encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "                # decoder的自注意力mask\n",
    "                # 因为是串行的，所以每次只看前面cur_len个token\n",
    "                attn_mask=look_ahead_mask[:, :, :cur_len, :cur_len],\n",
    "            )\n",
    "            # decoder_outputs.last_hidden_states: [batch_size, cur_len, hidden_size]\n",
    "            logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "            # logits: [batch_size, cur_len, vocab_size]\n",
    "            # 通过最大下标确定类别，[:, -1:]表示取最后一个结果\n",
    "            next_token = logits.argmax(dim=-1)[:, -1:]  # [batch_size, 1]\n",
    "            # 预测输出拼接到输入中\n",
    "            decoder_inputs = torch.cat([decoder_inputs, next_token], dim=-1)\n",
    "            # dcoder_inputs: [batch_size, cur_len+1]\n",
    "\n",
    "            # (decoder_inputs == self.eos_idx).sum(dim=-1)是判断样本中是否含有EOS标记\n",
    "            # all是每一个都为True，才会结束\n",
    "            if all((decoder_inputs == self.eos_idx).sum(dim=-1) > 0):\n",
    "                break\n",
    "\n",
    "        return TransformerOutput(\n",
    "            preds=decoder_inputs[:, 1:],  # 去掉bos_idx\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a740def7afa83f9",
   "metadata": {},
   "source": [
    "# 训练"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18175b791877293",
   "metadata": {},
   "source": [
    "## 损失函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a2b1b4622966eb25",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:48.587449Z",
     "start_time": "2025-02-07T11:31:48.582474Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:41:29.357527Z",
     "iopub.status.busy": "2025-02-07T16:41:29.357057Z",
     "iopub.status.idle": "2025-02-07T16:41:29.363125Z",
     "shell.execute_reply": "2025-02-07T16:41:29.362562Z",
     "shell.execute_reply.started": "2025-02-07T16:41:29.357495Z"
    }
   },
   "outputs": [],
   "source": [
    "class CrossEntropyWithPadding:\n",
    "    def __init__(self, config):\n",
    "        self.label_smoothing = config[\"label_smoothing\"]\n",
    "\n",
    "    def __call__(self, logits, labels, padding_mask=None):\n",
    "        # logits: [batch_size, seq_len, vocab_size]\n",
    "        # labels: [batch_size, seq_len]\n",
    "        # padding_mask: [batch_size, seq_len]\n",
    "        bs, seq_len, nc = logits.shape\n",
    "        loss = F.cross_entropy(\n",
    "            logits.reshape(bs * seq_len, nc),\n",
    "            labels.reshape(-1),\n",
    "            reduction='none',  # 不对损失进行归约（reduction\n",
    "            label_smoothing=self.label_smoothing\n",
    "        )  # label_smoothing表示随机将一个类别的概率设置为0.1，使得模型更加关注其他类别\n",
    "        # loss: [batch_size * seq_len, 1]\n",
    "\n",
    "        if padding_mask is None:\n",
    "            loss = loss.mean()  # 计算平均损失\n",
    "        else:\n",
    "            # 将padding_mask reshape成一维张量，mask部分为0，非mask部分为1\n",
    "            padding_mask = 1 - padding_mask.reshape(-1)\n",
    "            loss = torch.mul(loss, padding_mask).sum() / padding_mask.sum()\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab581173629b9cc",
   "metadata": {},
   "source": [
    "## 学习率衰减"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "39efc556d490f34e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:48.593329Z",
     "start_time": "2025-02-07T11:31:48.588456Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:41:29.364324Z",
     "iopub.status.busy": "2025-02-07T16:41:29.363988Z",
     "iopub.status.idle": "2025-02-07T16:41:29.368537Z",
     "shell.execute_reply": "2025-02-07T16:41:29.367904Z",
     "shell.execute_reply.started": "2025-02-07T16:41:29.364295Z"
    }
   },
   "outputs": [],
   "source": [
    "# NoamDecayScheduler 是一个自定义或外部定义的学习率衰减调度器类。\n",
    "# 它需要接收配置 config 作为参数，可能实现了特定的学习率衰减方案\n",
    "class NoamDecayScheduler:\n",
    "    def __init__(self, config):\n",
    "        self.d_model = config[\"d_model\"]\n",
    "        self.warmup_steps = config[\"warmup_steps\"]\n",
    "\n",
    "    # __call__ 方法是一个特殊的方法，用于使对象能够像函数一样被调用\n",
    "    def __call__(self, step):\n",
    "        step += 1\n",
    "        arg1 = step ** (-0.5)  # 4000步之后是arg1\n",
    "        arg2 = step * (self.warmup_steps ** (-1.5))  # 4000步之前是arg2\n",
    "        arg3 = self.d_model ** (-0.5)\n",
    "\n",
    "        return arg3 * np.minimum(arg1, arg2)  # minimum是取两个数中的较小值\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3188d631aaa09057",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:48.705200Z",
     "start_time": "2025-02-07T11:31:48.596339Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:41:29.369428Z",
     "iopub.status.busy": "2025-02-07T16:41:29.369211Z",
     "iopub.status.idle": "2025-02-07T16:41:29.477505Z",
     "shell.execute_reply": "2025-02-07T16:41:29.476994Z",
     "shell.execute_reply.started": "2025-02-07T16:41:29.369405Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_learning_rate_schedule = NoamDecayScheduler({\"d_model\": 512, \"warmup_steps\": 4000})\n",
    "#下面是学习率的设计图\n",
    "plt.plot(temp_learning_rate_schedule(np.arange(0, 40000)))\n",
    "plt.ylabel(\"Leraning rate\")\n",
    "plt.xlabel(\"Train step\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90185dd5b91d52ad",
   "metadata": {},
   "source": [
    "## 优化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a5061e4cd6464c1b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:48.710730Z",
     "start_time": "2025-02-07T11:31:48.706206Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:41:29.478346Z",
     "iopub.status.busy": "2025-02-07T16:41:29.478089Z",
     "iopub.status.idle": "2025-02-07T16:41:29.482266Z",
     "shell.execute_reply": "2025-02-07T16:41:29.481775Z",
     "shell.execute_reply.started": "2025-02-07T16:41:29.478325Z"
    }
   },
   "outputs": [],
   "source": [
    "from torch.optim.lr_scheduler import LambdaLR\n",
    "from torch.optim import Adam\n",
    "\n",
    "\n",
    "def get_optimizer(model, config):\n",
    "    base_lr = 0.1\n",
    "    beta1 = config[\"beta1\"]  # Adam 的 beta1\n",
    "    beta2 = config[\"beta2\"]  # Adam 的 beta2\n",
    "    eps = config[\"eps\"]\n",
    "    optimizer = Adam(model.parameters(), lr=base_lr, betas=(beta1, beta2), eps=eps)\n",
    "    # config是一个字典，包含了学习率衰减的参数\n",
    "    lr_scheduler = NoamDecayScheduler(config)\n",
    "    # 使用 LambdaLR 调度器，它可以根据给定的函数 lr_lambda 调整学习率。\n",
    "    # 这里将 lr_scheduler 作为函数传递给 LambdaLR，它包含了特定于模型或任务的学习率调度规则\n",
    "    scheduler = LambdaLR(optimizer, lr_lambda=lr_scheduler)\n",
    "    return optimizer, scheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "49a2f5433a409089",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:48.717338Z",
     "start_time": "2025-02-07T11:31:48.711735Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:41:29.483009Z",
     "iopub.status.busy": "2025-02-07T16:41:29.482828Z",
     "iopub.status.idle": "2025-02-07T16:41:29.488169Z",
     "shell.execute_reply": "2025-02-07T16:41:29.487675Z",
     "shell.execute_reply.started": "2025-02-07T16:41:29.482990Z"
    }
   },
   "outputs": [],
   "source": [
    "class SaveCheckpointsCallback:\n",
    "    def __init__(self, save_dir, save_step=5000, save_best_only=True):\n",
    "        self.save_dir = save_dir  # 保存路径\n",
    "        self.save_step = save_step  # 保存步数\n",
    "        self.save_best_only = save_best_only  # 是否只保存最好的模型\n",
    "        self.best_metric = -np.inf  # 最好的指标，指标不可能为负数，所以初始化为-1\n",
    "        # 创建保存路径\n",
    "        if not os.path.exists(self.save_dir):  # 如果不存在保存路径，则创建\n",
    "            os.makedirs(self.save_dir)\n",
    "\n",
    "    # 对象被调用时：当你将对象像函数一样调用时，Python 会自动调用 __call__ 方法。\n",
    "    # state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "    # metric 是指标，可以是验证集的准确率，也可以是其他指标\n",
    "    def __call__(self, step, state_dict, metric=None):\n",
    "        if step % self.save_step > 0:\n",
    "            return  # 不是保存步数，则直接返回\n",
    "\n",
    "        if self.save_best_only:\n",
    "            assert metric is not None  # 必须传入metric\n",
    "            if metric >= self.best_metric:  # 如果当前指标大于最好的指标\n",
    "                # save checkpoint\n",
    "                # 保存最好的模型，覆盖之前的模型，不保存step，只保存state_dict，即模型参数，不保存优化器参数\n",
    "                torch.save(state_dict, os.path.join(self.save_dir, \"MutilHeadAttention_embedding.ckpt\"))\n",
    "                self.best_metric = metric  # 更新最好的指标\n",
    "        else:\n",
    "            # 保存模型\n",
    "            torch.save(state_dict, os.path.join(self.save_dir, f\"{step}.ckpt\"))\n",
    "            # 保存每个step的模型，不覆盖之前的模型，保存step，保存state_dict，即模型参数，不保存优化器参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1c98380e43240291",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:48.723200Z",
     "start_time": "2025-02-07T11:31:48.718341Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:41:29.489132Z",
     "iopub.status.busy": "2025-02-07T16:41:29.488691Z",
     "iopub.status.idle": "2025-02-07T16:41:29.493010Z",
     "shell.execute_reply": "2025-02-07T16:41:29.492515Z",
     "shell.execute_reply.started": "2025-02-07T16:41:29.489111Z"
    }
   },
   "outputs": [],
   "source": [
    "class EarlyStopCallback:\n",
    "    def __init__(self, patience=5, min_delta=0.01):\n",
    "        self.patience = patience  # 多少个step没有提升就停止训练\n",
    "        self.min_delta = min_delta  # 最小的提升幅度\n",
    "        self.best_metric = -np.inf  # 记录的最好的指标\n",
    "        self.counter = 0  # 计数器，记录连续多少个step没有提升\n",
    "\n",
    "    def __call__(self, metric):\n",
    "        if metric >= self.best_metric + self.min_delta:  # 如果指标提升了\n",
    "            self.best_metric = metric  # 更新最好的指标\n",
    "            self.counter = 0  # 计数器清零\n",
    "        else:\n",
    "            self.counter += 1  # 计数器加一\n",
    "\n",
    "    @property  # 使用@property装饰器，使得 对象.early_stop可以调用，不需要()\n",
    "    def early_stop(self):\n",
    "        # 如果计数器大于等于patience，则返回True，停止训练\n",
    "        return self.counter >= self.patience"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebb3c8c230d312c1",
   "metadata": {},
   "source": [
    "## training & valuating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "140f2acfbeb9740a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:48.729134Z",
     "start_time": "2025-02-07T11:31:48.724211Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:41:29.493864Z",
     "iopub.status.busy": "2025-02-07T16:41:29.493532Z",
     "iopub.status.idle": "2025-02-07T16:41:29.497824Z",
     "shell.execute_reply": "2025-02-07T16:41:29.497313Z",
     "shell.execute_reply.started": "2025-02-07T16:41:29.493845Z"
    }
   },
   "outputs": [],
   "source": [
    "@torch.no_grad()  # 禁用梯度计算\n",
    "def evaluating(model, data_loader, loss_fct):\n",
    "    loss_list = []\n",
    "    for batch in data_loader:\n",
    "        encoder_inputs = batch[\"encoder_inputs\"]\n",
    "        encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "        decoder_inputs = batch[\"decoder_inputs\"]\n",
    "        decoder_labels = batch[\"decoder_labels\"]\n",
    "        decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "        # 前向计算\n",
    "        outputs = model(\n",
    "            encoder_inputs=encoder_inputs,\n",
    "            decoder_inputs=decoder_inputs,\n",
    "            encoder_inputs_mask=encoder_inputs_mask,\n",
    "        )\n",
    "        logits = outputs.logits\n",
    "        # 计算损失\n",
    "        loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "        loss_list.append(loss.cpu().item())\n",
    "\n",
    "    return np.mean(loss_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3099e48677dcb6bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:48.738176Z",
     "start_time": "2025-02-07T11:31:48.731141Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:41:29.498612Z",
     "iopub.status.busy": "2025-02-07T16:41:29.498401Z",
     "iopub.status.idle": "2025-02-07T16:41:29.506849Z",
     "shell.execute_reply": "2025-02-07T16:41:29.506389Z",
     "shell.execute_reply.started": "2025-02-07T16:41:29.498593Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def training(model,\n",
    "             train_loader,\n",
    "             val_loader,\n",
    "             epoch,\n",
    "             loss_fct,\n",
    "             optimizer,\n",
    "             scheduler=None,\n",
    "             save_ckpt_callback=None,\n",
    "             early_stop_callback=None,\n",
    "             eval_step=500,\n",
    "             ):\n",
    "    record_dict = {\n",
    "        \"train\": [],\n",
    "        \"val\": []\n",
    "    }\n",
    "\n",
    "    global_step = 1  # 全局步数\n",
    "    model.train()  # 训练模式\n",
    "    with tqdm(total=epoch * len(train_loader)) as pbar:\n",
    "        for epoch_id in range(epoch):\n",
    "            # 训练\n",
    "            for batch in train_loader:\n",
    "                encoder_inputs = batch[\"encoder_inputs\"]\n",
    "                encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "                decoder_inputs = batch[\"decoder_inputs\"]\n",
    "                decoder_labels = batch[\"decoder_labels\"]\n",
    "                decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "                optimizer.zero_grad()  # 梯度清零\n",
    "\n",
    "                # 前向传播\n",
    "                outputs = model(\n",
    "                    encoder_inputs=encoder_inputs,\n",
    "                    decoder_inputs=decoder_inputs,\n",
    "                    encoder_inputs_mask=encoder_inputs_mask\n",
    "                )\n",
    "                logits = outputs.logits\n",
    "                loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "\n",
    "                # 梯度回传\n",
    "                loss.backward()\n",
    "\n",
    "                # 调整优化器，包括学习率的变动等\n",
    "                optimizer.step()\n",
    "                if scheduler is not None:\n",
    "                    scheduler.step()  # 更新学习率\n",
    "\n",
    "                loss = loss.cpu().item()\n",
    "\n",
    "                record_dict[\"train\"].append({\n",
    "                    \"loss\": loss,\n",
    "                    \"step\": global_step\n",
    "                })\n",
    "\n",
    "                # 评估\n",
    "                if global_step % eval_step == 0:\n",
    "                    model.eval()  # 评估模式\n",
    "                    # 验证集损失和准确率\n",
    "                    val_loss = evaluating(model, val_loader, loss_fct)\n",
    "                    record_dict[\"val\"].append({\n",
    "                        \"loss\": val_loss,\n",
    "                        \"step\": global_step\n",
    "                    })\n",
    "                    model.train()  # 训练模式\n",
    "\n",
    "                    # 2. 保存模型权重 save model checkpoint\n",
    "                    if save_ckpt_callback is not None:\n",
    "                        # model.state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "                        save_ckpt_callback(global_step, model.state_dict(), -val_loss)\n",
    "                        # 保存最好的模型，覆盖之前的模型，保存step，保存state_dict,通过metric判断是否保存最好的模型\n",
    "\n",
    "                    # 3. 早停 early stopping\n",
    "                    if early_stop_callback is not None:\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        early_stop_callback(-val_loss)\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        if early_stop_callback.early_stop:\n",
    "                            print(f\"Early stop at epoch {epoch_id} / global_step {global_step}\")\n",
    "                            return record_dict  # 早停，返回记录字典 record_dict\n",
    "\n",
    "                # 更新进度条和全局步数\n",
    "                pbar.update(1)  # 更新进度条\n",
    "                global_step += 1  # 全局步数加一\n",
    "            pbar.set_postfix({\"epoch\": epoch_id, \"loss\": loss, \"val_loss\": val_loss})\n",
    "\n",
    "    return record_dict  # 训练结束，返回记录字典 record_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cf04ac5e18afea4f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:49.004249Z",
     "start_time": "2025-02-07T11:31:48.739179Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:41:29.507684Z",
     "iopub.status.busy": "2025-02-07T16:41:29.507463Z",
     "iopub.status.idle": "2025-02-07T16:41:29.682606Z",
     "shell.execute_reply": "2025-02-07T16:41:29.682092Z",
     "shell.execute_reply.started": "2025-02-07T16:41:29.507665Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load train dataset from wmt16/.cache/de2en_train_128.npy\n",
      "load val dataset from wmt16/.cache/de2en_val_128.npy\n"
     ]
    }
   ],
   "source": [
    "#模型的超参\n",
    "config = {\n",
    "    \"bos_idx\": 1,\n",
    "    \"eos_idx\": 3,\n",
    "    \"pad_idx\": 0,\n",
    "    \"vocab_size\": len(word2idx),\n",
    "    \"max_length\": 128,\n",
    "    \"d_model\": 512, # 可调整\n",
    "    \"dim_feedforward\": 2048,  # FFN 的隐藏层大小\n",
    "    \"dropout\": 0.1, # 可调整\n",
    "    \"layer_norm_eps\": 1e-6,  # 层归一化的 epsilon, 防止除零错误\n",
    "    \"num_heads\": 8, # 可调整\n",
    "    \"num_decoder_layers\": 6, # 可调整\n",
    "    \"num_encoder_layers\": 6, # 可调整\n",
    "    \"label_smoothing\": 0.1,\n",
    "    \"beta1\": 0.9,  # Adam 的 beta1\n",
    "    \"beta2\": 0.98,  # Adam 的 beta2\n",
    "    \"eps\": 1e-9,\n",
    "    \"warmup_steps\": 4_000,\n",
    "    \"share_embedding\": False,  # 是否共享词向量\n",
    "}\n",
    "\n",
    "\n",
    "def get_dl(dataset, batch_size, shuffle=True):\n",
    "    sampler = TransformerBatchSampler(dataset, batch_size=batch_size, shuffle_batch=shuffle)\n",
    "    sample_dl = DataLoader(dataset, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "    return sample_dl\n",
    "\n",
    "\n",
    "# dataset\n",
    "train_ds = LangPairDataset(\"train\", max_length=config[\"max_length\"])\n",
    "val_ds = LangPairDataset(\"val\", max_length=config[\"max_length\"])\n",
    "\n",
    "# tokenizer\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word, max_length=config[\"max_length\"])\n",
    "\n",
    "# dataloader\n",
    "batch_size = 2048\n",
    "train_dl = get_dl(train_ds, batch_size=batch_size, shuffle=True)\n",
    "val_dl = get_dl(val_ds, batch_size=batch_size, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c4bd1cb45f5032ee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:31:49.989080Z",
     "start_time": "2025-02-07T11:31:49.005270Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:41:29.683357Z",
     "iopub.status.busy": "2025-02-07T16:41:29.683171Z",
     "iopub.status.idle": "2025-02-07T16:41:30.898137Z",
     "shell.execute_reply": "2025-02-07T16:41:30.897442Z",
     "shell.execute_reply.started": "2025-02-07T16:41:29.683337Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型参数量: 90812607\n"
     ]
    }
   ],
   "source": [
    "#计算模型参数量\n",
    "model = TransformerModel(config)\n",
    "print(f\"模型参数量: {sum(p.numel() for p in model.parameters() if p.requires_grad)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6b87e4917b9ef89b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:32:05.396130Z",
     "start_time": "2025-02-07T11:31:49.992083Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T16:41:30.899021Z",
     "iopub.status.busy": "2025-02-07T16:41:30.898766Z",
     "iopub.status.idle": "2025-02-07T17:06:03.679668Z",
     "shell.execute_reply": "2025-02-07T17:06:03.679009Z",
     "shell.execute_reply.started": "2025-02-07T16:41:30.898999Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "31999it [24:30, 21.76it/s, epoch=29, loss=2.36, val_loss=3.47]                        "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stop at epoch 30 / global_step 32000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "epoch = 100\n",
    "\n",
    "# model\n",
    "model = TransformerModel(config)\n",
    "model = model.to(device)\n",
    "\n",
    "# 1. 定义损失函数 采用交叉熵损失\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "# 2. 定义优化器\n",
    "optimizer, scheduler = get_optimizer(model, config)\n",
    "\n",
    "# 3.save model checkpoint\n",
    "if not os.path.exists(\"checkpoints\"):\n",
    "    os.makedirs(\"checkpoints\")\n",
    "save_ckpt_callback = SaveCheckpointsCallback(save_dir=\"checkpoints\", save_step=500, save_best_only=True)\n",
    "\n",
    "# 4. early stopping\n",
    "early_stop_callback = EarlyStopCallback(patience=10,min_delta=0.001)\n",
    "\n",
    "record_dict = training(\n",
    "    model,\n",
    "    train_dl,\n",
    "    val_dl,\n",
    "    epoch,\n",
    "    loss_fct,\n",
    "    optimizer,\n",
    "    scheduler,\n",
    "    save_ckpt_callback=save_ckpt_callback,\n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=500\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50ac7e66d5df05c3",
   "metadata": {},
   "source": [
    "# 推理\n",
    "\n",
    "- 翻译项目的评估指标一般是BLEU4\n",
    "- 接下来进行翻译推理，并作出注意力的热度图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "305c0410623a9f8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:30:21.109129Z",
     "start_time": "2025-02-07T11:30:21.109129Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T17:06:03.681505Z",
     "iopub.status.busy": "2025-02-07T17:06:03.680819Z",
     "iopub.status.idle": "2025-02-07T17:06:03.869339Z",
     "shell.execute_reply": "2025-02-07T17:06:03.868797Z",
     "shell.execute_reply.started": "2025-02-07T17:06:03.681471Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "# 加载模型\n",
    "state_dict = torch.load(f\"checkpoints/MutilHeadAttention_embedding.ckpt\", map_location=\"cpu\",weights_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6f512e7c8f2db972",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:30:21.111129Z",
     "start_time": "2025-02-07T11:30:21.110129Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T17:06:03.870632Z",
     "iopub.status.busy": "2025-02-07T17:06:03.870071Z",
     "iopub.status.idle": "2025-02-07T17:06:14.890382Z",
     "shell.execute_reply": "2025-02-07T17:06:14.889833Z",
     "shell.execute_reply.started": "2025-02-07T17:06:03.870598Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load test dataset from wmt16/.cache/de2en_test_128.npy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 3-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 4-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "10it [00:00, 92.89it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 2-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "966it [00:09, 101.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing loss: 3.33418073269151\n",
      "testing bleu: 0.5829534455382283\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from nltk.translate.bleu_score import sentence_bleu\n",
    "\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "test_ds = LangPairDataset(\"test\", max_length=config[\"max_length\"])\n",
    "test_dl = DataLoader(\n",
    "    test_ds, batch_size=1,\n",
    "    collate_fn=partial(collate_fn, tokenizer=tokenizer)\n",
    ")\n",
    "\n",
    "model = model.to(device)\n",
    "model.eval()\n",
    "collect = {}\n",
    "loss_collect = []\n",
    "\n",
    "predictions = []\n",
    "answers = []\n",
    "\n",
    "# 初始化BLEU分数列表\n",
    "bleu_scores = []\n",
    "for idx, batch in tqdm(enumerate(test_dl)):\n",
    "    encoder_inputs = batch[\"encoder_inputs\"]\n",
    "    encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "    decoder_inputs = batch[\"decoder_inputs\"]\n",
    "    decoder_labels = batch[\"decoder_labels\"]\n",
    "\n",
    "    # 前向计算\n",
    "    outputs = model(\n",
    "        encoder_inputs=encoder_inputs,\n",
    "        decoder_inputs=decoder_inputs,\n",
    "        encoder_inputs_mask=encoder_inputs_mask\n",
    "    )\n",
    "\n",
    "    loss = loss_fct(outputs.logits, decoder_labels)  # 验证集损失\n",
    "\n",
    "    preds = outputs.logits.argmax(dim=-1)  # 预测结果，[1,seq_len]\n",
    "    # 把preds转为英文单词\n",
    "    preds = tokenizer.decode(preds.cpu().numpy())  #['预测句子']\n",
    "    #把decoder_labels转为英文单词\n",
    "    decoder_labels = tokenizer.decode(decoder_labels.cpu().numpy())  #['标签句子']\n",
    "\n",
    "    answers.append(decoder_labels)\n",
    "\n",
    "    belu = sentence_bleu([decoder_labels[0].split()], preds[0].split(), weights=(1, 0, 0, 0))\n",
    "    bleu_scores.append(belu)\n",
    "    collect[idx] = {\n",
    "        \"loss\": loss.item(),\n",
    "        \"src_inputs\": encoder_inputs,\n",
    "        \"trg_inputs\": decoder_inputs,\n",
    "        \"mask\": encoder_inputs_mask,\n",
    "        \"trg_labels\": decoder_labels,\n",
    "        \"preds\": preds\n",
    "    }\n",
    "    loss_collect.append(loss.item())\n",
    "\n",
    "# sort collect by value\n",
    "collect = sorted(collect.items(), key=lambda x: x[1][\"loss\"])\n",
    "print(f\"testing loss: {np.array(loss_collect).mean()}\")\n",
    "belu_scores = sum(bleu_scores) / len(bleu_scores)\n",
    "print(f\"testing bleu: {belu_scores}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a9ab2039d24b72a3",
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
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     "shell.execute_reply": "2025-02-07T17:06:14.893309Z",
     "shell.execute_reply.started": "2025-02-07T17:06:14.891253Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# !pip install Cython  # if failed to install fastBPE, try this line\n",
    "# !pip install fastBPE -v\n",
    "# 在 Windows 系统上并没有 sys/mman.h 文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1e7c1e0999eb365d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-07T11:30:21.113130Z",
     "start_time": "2025-02-07T11:30:21.113130Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T17:06:14.894693Z",
     "iopub.status.busy": "2025-02-07T17:06:14.894515Z",
     "iopub.status.idle": "2025-02-07T17:06:15.262438Z",
     "shell.execute_reply": "2025-02-07T17:06:15.261913Z",
     "shell.execute_reply.started": "2025-02-07T17:06:14.894673Z"
    }
   },
   "outputs": [],
   "source": [
    "import re\n",
    "from fastBPE import fastBPE\n",
    "from sacremoses import MosesDetokenizer, MosesTokenizer\n",
    "\n",
    "# `MosesTokenizer` 和 `MosesDetokenizer` 是来自 `sacremoses` 库的工具，用于自然语言处理中的分词（Tokenization）和去标记化（Detokenization）。\n",
    "# 这些工具主要用于对文本进行预处理和后处理，通常在处理自然语言处理任务时会用到。\n",
    "\n",
    "# 这些工具通常在文本预处理和后处理过程中使用，对输入的文本进行标记化和去标记化，是一种常用的处理方式。\n",
    "# 在自然语言处理任务中，对文本进行正确的分词和还原是很重要的，而 `MosesTokenizer` 和 `MosesDetokenizer` 提供了方便、高效的工具来处理这些任务。\n",
    "\n",
    "class Translator:\n",
    "    def __init__(self, model, src_tokenizer, trg_tokenizer):\n",
    "        self.bpe = fastBPE(\"./wmt16/bpe.20000\", \"./wmt16/vocab\")\n",
    "        self.mose_tokenizer = MosesTokenizer(lang=\"de\")\n",
    "        self.mose_detokenizer = MosesDetokenizer(lang=\"en\")\n",
    "        self.model = model\n",
    "        self.model.eval()\n",
    "        self.src_tokenizer = src_tokenizer\n",
    "        self.trg_tokenizer = trg_tokenizer\n",
    "        # 匹配出现的 @@ （后面带空格的情况）。\n",
    "        # 或者匹配以 @@ 结尾的情况（可选空格）。\n",
    "        self.pattern = re.compile(r'(@@ )|(@@ ?$)')\n",
    "        \n",
    "    def draw_attention_map(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "        attn_scores (numpy.ndarray): 表示自注意力机制（self-attention）分数。\n",
    "        cross_attn_scores (numpy.ndarray): 表示交叉注意力机制的注意力分数。\n",
    "        src_words_list (list): 源语言句子的单词列表。\n",
    "        trg_words_list (list): 目标语言句子的单词列表。\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "\n",
    "        fig = plt.figure(figsize=(10, 5), constrained_layout=True) # constrained_layout=True 自动调整子图参数，使之填充整个图像区域\n",
    "        grid = plt.GridSpec(trg_len, trg_len + src_len, wspace=0.1, hspace=0.1)# wspace,hspace 控制子图之间的间距\n",
    "        #下面是attn_scores的热力图\n",
    "        self_map = fig.add_subplot(grid[:,:trg_len]) #  添加子图\n",
    "        self_map.matshow(attn_scores.mean(dim=0), cmap='viridis') # 绘制热力图，cmap表示颜色,dim=0表示对第0维求均值\n",
    "        self_map.set_yticks(range(trg_len), trg_words_list, fontsize=10)\n",
    "        self_map.set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "        #下面是cross_attn_scores的热力图\n",
    "        cross_map = fig.add_subplot(grid[:, trg_len:])\n",
    "        cross_map.matshow(cross_attn_scores.mean(dim=0), cmap='viridis')\n",
    "        cross_map.set_yticks(range(trg_len), [], fontsize=6)\n",
    "        cross_map.set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "    def draw_attention_maps(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list, heads_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "\n",
    "        Args:\n",
    "            - scores (numpy.ndarray): shape = [source sequence length, target sequence length]\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "        # cross_attn_scores = cross_attn_scores[:, :len(src_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "        fig, axes = plt.subplots(2, len(heads_list), figsize=(5 * len(heads_list), 10))\n",
    "        for i, heads_idx in enumerate(heads_list):\n",
    "            axes[0, i].matshow(attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[0, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[0, i].set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "            axes[0, i].set_title(f\"head {heads_idx}\")\n",
    "            axes[1, i].matshow(cross_attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[1, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[1, i].set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "            axes[1, i].set_title(f\"head {heads_idx}\")\n",
    "\n",
    "        plt.show()\n",
    "    \n",
    "    def __call__(self, sentence_list,heads_list=None,layer_idx=-1):\n",
    "        # 将输入句子列表转换为小写，并使用 MosesTokenizer 进行分词处理。\n",
    "        sentence_list = [\" \".join(self.mose_tokenizer.tokenize(s.lower())) for s in sentence_list]\n",
    "        # 将分词后的结果进行 BPE 编码，得到 tokens_list。\n",
    "        tokens_list = [s.split() for s in self.bpe.apply(sentence_list)]\n",
    "        \n",
    "        # 使用 src_tokenizer 对 tokens_list 进行编码，同时添加起始标记 ([BOS]) 和结束标记 ([EOS])。\n",
    "        encoder_input, attn_mask = self.src_tokenizer.encode(\n",
    "            tokens_list,\n",
    "            add_bos=True,\n",
    "            add_eos=True,\n",
    "            return_mask=True,\n",
    "            )\n",
    "        encoder_input = torch.Tensor(encoder_input).to(dtype=torch.int64)\n",
    "        \n",
    "        # 使用模型的 infer 方法对编码器输入进行推理，得到输出结果 outputs\n",
    "        outputs = model.infer(encoder_inputs=encoder_input, encoder_inputs_mask=attn_mask)\n",
    "        preds = outputs.preds.numpy() # 推理结果\n",
    "        \n",
    "        # 使用目标语言的 trg_tokenizer 对预测序列进行解码，得到解码后的目标语言句子列表 trg_decoded。\n",
    "        trg_decoded = self.trg_tokenizer.decode(preds, split=True, remove_eos=False, remove_bos=False, remove_pad=False)\n",
    "        # 使用源语言的 src_tokenizer 对编码器输入进行解码，得到解码后的源语言句子列表 src_decoded。为下面绘制热力图做准备。\n",
    "        src_decoded = self.src_tokenizer.decode(\n",
    "            encoder_input.numpy(),\n",
    "            split=True,\n",
    "            remove_bos=False,\n",
    "            remove_eos=False\n",
    "            )\n",
    "        \n",
    "         # draw the attention map of the last decoder block\n",
    "        for attn_score, cross_attn_score, src, trg in zip(\n",
    "            outputs.decoder_self_attn_scores[layer_idx], outputs.decoder_cross_attn_scores[layer_idx], src_decoded, trg_decoded):\n",
    "            if heads_list is None:# 如果没有指定heads_list，就画单个热力图\n",
    "                self.draw_attention_map(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                )\n",
    "            else:# 如果指定了heads_list，就画多个热力图\n",
    "                self.draw_attention_maps(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                    heads_list=heads_list,\n",
    "                    )\n",
    "        \n",
    "        #将解码后的目标语言句子列表返回，并使用 mose_detokenizer 进行去标记化，最终得到翻译后的结果。\n",
    "        return [self.mose_detokenizer.tokenize(self.pattern.sub(\"\", s).split()) for s in self.trg_tokenizer.decode(preds)] \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c5d288cf83d67b62",
   "metadata": {
    "ExecuteTime": {
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     "start_time": "2025-02-07T11:30:21.114130Z"
    },
    "ExecutionIndicator": {
     "show": false
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T17:06:15.263465Z",
     "iopub.status.busy": "2025-02-07T17:06:15.263037Z",
     "iopub.status.idle": "2025-02-07T17:06:16.808803Z",
     "shell.execute_reply": "2025-02-07T17:06:16.808258Z",
     "shell.execute_reply.started": "2025-02-07T17:06:15.263443Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading vocabulary from ./wmt16/vocab ...\n",
      "Read 762259 words (18107 unique) from vocabulary file.\n",
      "Loading codes from ./wmt16/bpe.20000 ...\n",
      "Read 20001 codes from the codes file.\n",
      "  8%|▊         | 10/128 [00:00<00:01, 61.05it/s]\n",
      "/usr/local/lib/python3.10/site-packages/IPython/core/pylabtools.py:170: UserWarning: There are no gridspecs with layoutgrids. Possibly did not call parent GridSpec with the \"figure\" keyword\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "['a man and a woman are having a meal.']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentence_list = [\n",
    "    # \"Mann in einem kleinen weißen Boot auf einem See.\",  # Man in a small white boat on a lake. ans:['man in a white boat on a boat.']\n",
    "    # \"Ein Mann mit einem Eimer und ein Mädchen mit einem Hut am Strand.\", # A man with a bucket and a girl in a hat on the beach. ans:['a man with a girl and a girl on the beach with a fishing pole.']\n",
    "    # \"Drei Männer auf Pferden während eines Rennens.\",  # Three men on horses during a race. ans:['three men on horses are running in a race.']\n",
    "    \"Ein Mann und eine Frau essen zu Abend\",  # 一个男人和一个女人在吃晚餐 # ['a man and a woman are eating food.']\n",
    "]\n",
    "\n",
    "# load checkpoints\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "translator = Translator(model.cpu(), tokenizer, tokenizer)\n",
    "translator(\n",
    "    sentence_list,\n",
    "    layer_idx=-1,\n",
    "    # heads_list=[0, 1, 2, 3, 4, 5, 6, 7]\n",
    "    )"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
